Blood glucose level monitoring is of great importance for diabetics. Continuous monitoring of the glucose level can greatly reduce the medical complications, that are caused by metabolic imbalance.
The Islets of Langerhans are located in the pancreas, and are responsible for the manufacture of insulin in the human body. An islet is a cluster of many cells. The Beta cells within the islets respond to glucose in bursts of electrical activity. The use of such islets, derived from the pancreas of donor animals, in a blood glucose monitor is disclosed in U.S. Pat. No. 5,101,814. Electro-Islet-Graphy (EIG) is the measurement of the electrical activity of the islets of Langerhans. The present invention utilizes EIG to provide a continuous blood glucose level sensor.
Studies demonstrate a clear correlation between the fundamental frequency of the EIG signal, and the glucose level in the medium surrounding the islet. Hence, the estimation of the fundamental frequency of Electro-Islet-Graphy is of significant practical value.
The fundamental frequency is defined as the frequency of the “events” of the EIG. An “event” is believed to represent the synchronized electrical activity of the cells in the islet. By analogy to an ECG, an “event” in EIG is comparable to a the heart cycle (the PQRST comlex).
Although it is believed that EIG processing has not been performed by any entity but the owner the present patent application, one might attempt to detect the events directly, and then to calculate the fundamental frequency. The problem with this approach is that the shape and size of EIG “events” varies greatly, and therefore reliable and robust event detection is difficult to achieve.
In accordance with the present invention, the fundamental frequency of EIG events is determined directly, without first detecting the individual events themselves. All these algorithms must use an analysis window containing more than one event. The present invention utilizes techniques similar to those utilized to detect and estimate pitch in speech processing
The invention is based on two important biological discoveries. The first is that the EIG is generated by a functional pace maker. The second is that the EIG signal is quasi-periodic most of the time. Pitch detection algorithms are used, because of the essentially quasi-periodic nature of the EIG signal. By quasi-periodic we mean that (1) the intervals between successive events are not exactly identical, but may vary slightly and (2) the amplitude and shape of successive events may also exhibit some variance.
Several pitch detection algorithms were tested. Three of them achieved good results: Autocorrelation, Segmented Autocorrelation and Harmonic Peaks analysis. The preferred embodiments of the invention focus on algorithms that are based on the Autocorrelation methods.
The preferred version of the algorithm comprises the following steps:                Detection of the non-EIG signals. The signal may include non-EIG segments, such as artifacts and silences. The signal is scanned and the non-EIG segments are marked and ignored.                    The signal is divided into overlapping analysis windows, each four seconds long and each has a 75% overlap with the adjacent windows. The analysis window contains more than one event.            A modified form of an Autocorrelation transform of the type used for pitch detection in speech processing is applied to a single analysis window. This step is repeated for each analysis window.            The fundamental frequency is derived from the autocorrelation values of the analysis window. Usually the fundamental frequency is indicated by the largest autocorrelation value. This step is repeated for each analysis window.            A postprocessor is used to “smooth out” the results of all the individual analysis windows. The previously marked non-EIG segments (artifacts and silences) are added in this phase.                        
To produce the modified autocorrelation transform, the existing autocorrelation based algorithm was adapted to EIG in the following ways:                                    An improved algorithm was devised for determining pitch from the autocorrelation values. The algorithm usually chooses the highest autocorrelation peak (value). In EIG we found that sometimes the true pitch is not represented by a peak, but rather by a valley between several adjacent peaks. An algorithm was devised to locate those cases, and to correctly estimate the pitch. We refer to this phenomena as a “volcano” shaped autocorrelation graph, because the center of the “mountain” is found on lower ground.            A voiced/unvoiced decision mechanism was adapted from speech processing. The “unvoiced” EIG segments were defined as a non-signals (undecided segments). A postprocessor was used to decide on the pitch of those undecided segments. Although unvoiced speech segments do exist, “unvoiced” EIG segments are a virtual non-signal, and do not really exist.            A special pre-processing algorithm was devised. The signal undergoes convolution so as to increase the width of the event. This is unlike speech pre-processing, which is aimed at enhancing the high amplitude portions and/or filtering out the formants. This preprocessing technique is referred to as “fattening”.            A very long analysis window of 4 seconds was used, in order to find frequencies ranging from 0.25 Hz to 5 Hz. In speech it is customary to use a window of about 30 milliseconds, in order to find frequencies ranging from 80 Hz to 300 Hz.                        
The invention also contemplates the use of a Segmented Autocorrelation algorithm. This is considered to be the best method for EIG, but other methods are also adequate. Segmented autocorrelation is described in “Pitch detection of speech signals using Segmented Autocorrelation”/I. A. Atkinson, A. M. Kondoz, B. G. Evans/Electronics Letters Vol. 31 No. 7 pp. 533–535/March 1995.